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Found 72 Skills
Independence-validated parallel fleet that runs each worker (claude -p or codex exec) in its own git worktree. Use when tasks touch non-overlapping files and you need merge-safe isolation (each worker on its own branch). For DAG-ordered one-shot workers with budgets, use dag-fleet. For headless iteration with a reviewer loop, use iterative-fleet.
Analyze an existing codebase with parallel mapper agents, creating codebase documentation, understanding brownfield projects, or mapping code structure. Triggers include "map codebase", "analyze codebase", "create project context", "document codebase", "understand code", and "codebase map".
6-phase investigation workflow for understanding existing systems. Auto-activates for research tasks. Optimized for exploration and understanding, not implementation. Includes parallel agent deployment for efficient deep dives and automatic knowledge capture to prevent repeat investigations.
Launch 3 research agents in parallel — market, users, tech — fast answers
Generate multiple radically different interface designs for a module using parallel sub-agents. Use when user wants to design an API, explore interface options, compare module shapes, or mentions "design it twice".
Dispatch and coordinate parallel agent execution. Manages concurrent task processing with result aggregation and error handling.
Comprehensive codebase quality audit with parallel agent orchestration, GitHub issue creation, automated PR generation per issue, and PM-prioritized recommendations. Use for code review, refactoring audits, technical debt analysis, module quality assessment, or codebase health checks.
Use when user wants to find a note to publish as a blog post. Triggers on「选一篇笔记发博客」「note to blog」「写博客」「博客选题」. Scans Obsidian notes via Python script, evaluates blog-readiness, supports batch selection with fast/deep dual-track and parallel Agent dispatch.
Conduct web research and material downloading for each node. Read node-list.txt, launch multiple sub-agents to perform parallel web research on node content, deeply retrieve relevant webpages/articles/blogs/literature, download and save them locally, and output a download.txt file to record the material sources for each node. Suitable for document writing scenarios that require extensive background information, data verification, and reference sources.
Use this method when fact-checking drafts that include dates, quantities, or causal claims by cross-referencing multiple independent sources.
Autonomous TDD development loop with parallel agent swarm, category evolution, and convergence detection. Use when running autonomous game development, quality improvement loops, or comprehensive codebase reviews.
Design exploration with parallel agents. Use when brainstorming ideas, exploring solutions, or comparing alternatives.